Children undergoing neurosurgery for treatment of intractable epilepsy are at significant risk for developing functional deficits, including post-operative aphasia. Morbidity can be minimized through careful mapping of eloquent cortex and seizure generating tissue. Increasingly, clinicians rely on neuroimaging, including functional MRI (fMRI) and magnetoencephalography (MEG), to provide information about language representation, prior to resection. Neuroimaging approaches are safe, cost effective, and repeatable, as necessary. However, conventional analyses fail to produce maps that can be used unambiguously to define resective margins. We are proposing connectivity-based mapping of critical language sites using MEG. Uniquely, MEG offers fine spatial resolution in combination with sub-millisecond temporal resolution, providing access to local neuronal dynamics and the resulting connectivity patterns that are believed to support all higher cognitive processes. From fast MEG recordings, we can capture both functional (undirected) and effective (directed) connections, thus gaining insight into how the network is integrated, as well as the causal relationships among constituent regions. We will characterize information exchange among discrete neuronal populations (nodes) supporting language, and identify sites that are critical (hubs) for network function. The goal of this study is to optimize and validate a noninvasive neuroimaging pipeline that can be used to identify the margins of language cortex in single subjects. The long-term goal of this line of research is to develop fully-noninvasive, neuroimaging-based methods for precise mapping of eloquent function. The analytic pipeline we have developed is domain-general, and could potentially be applied for mapping other cognitive functions, such as memory. With MEG, we can study both patients and healthy individuals across the lifespan; our approach promises to advance theory related to the normal architecture, dynamics, and plasticity of the brain.